> nigeria <- read.csv("nigeria2012.csv", header = FALSE)
> nigeria$degree <- NA
> for (i in seq(2, 2756, 2)) {
+ nigeria$degree[i - 1] <- as.numeric(as.character(nigeria$V2[i]))
+ }
> nigeria <- nigeria[nigeria$V1 != unique(nigeria$V1)[2], ]
> names(nigeria) <- c("lastname1", "lastname2", "degree")
> nigeria.b <- nigeria[1:1326, c("lastname2", "lastname1", "degree")]
> names(nigeria.b) <- c("lastname1", "lastname2", "degree")
> nigeria2 <- data.frame(rbind(nigeria, nigeria.b))
> nigeria2 <- nigeria2 %>% arrange(lastname2)
> nigeria2 <- nigeria2 %>% arrange(lastname1)
> nwk.s <- reshape(nigeria2, v.names = "degree", timevar = "lastname2", 
+ direction = "wide", idvar = "lastname1")
> nwk.s <- as.matrix(nwk.s[1:nrow(nwk.s), 2:ncol(nwk.s)])
> cent <- read.csv("nigerialist2012.csv", header = TRUE)
> cent <- cent %>% arrange(labels)
> rownames(nwk.s) <- cent$labels
> colnames(nwk.s) <- cent$labels
> shortnames <- function(x, y = cent$labels) {
+ str_split(y, " ")[[x]][length(str_split(y, " ")[[x]])]
+ }
> short <- sapply(X = 1:52, FUN = shortnames)
> nwk <- network(x = nwk.s, directed = FALSE, matrix.type = "a", 
+ ignore.eval = FALSE, names.eval = "hits")
> nigeria3 <- as.matrix(nwk, attrname = "hits", matrix.type = "edgelist")
> nwk1 <- nwk
> delete.edges(nwk1, seq_along(nwk1$mel))
> nwk1[nigeria3[, 1:2], names.eval = "hits", add.edges = TRUE] <- nigeria3[, 
+ 3]
> k <- max(log(as.matrix(nwk1, attrname = "hits") + 1))
> nwk.col <- matrix(gray(1 - (log(as.matrix(nwk1, attrname = "hits") + 
+ 1)/k)), nrow = network.size(nwk1))
> nwk.width <- log(as.matrix(nwk1, attrname = "hits") + 1)
> nwk.type <- as.numeric(cent$type) + 1
> nwk.board <- as.numeric(cent$board) + 1
> nwk.board[nwk.board == 3] <- 0
> nwk.size <- cent$selfhits <- log(nigeria[1327:1378, 3] + 1)
> leftplotcoord <- read.csv(file = "leftplotcoord.csv")
> pdf(file = "Figure1-left.pdf", width = 7, height = 7)
> plot(nwk1, edge.col = nwk.col, usecurve = TRUE, displayisolates = TRUE, 
+ coord = leftplotcoord, edge.curve = 0.01, edge.lwd = nwk.width, 
+ vertex.col = nwk.board + 2, vertex.lty = 0)
> segments(x0 = 21.65, y0 = -24, x1 = 21, y1 = -32, lwd = 2, lty = 2, 
+ col = 2)
> mtext(text = "Jonathan", side = 1, col = 2)
> dev.off()
null device 
          1 
> nwk.s <- log(nwk.s + 1)
> cent$net.bw <- betweenness(nwk.s, cmode = "undirected", ignore.eval = FALSE)
> cent$net.close <- closeness(nwk.s, cmode = "undirected", ignore.eval = FALSE)
> cent$net.ev <- evcent(nwk.s, ignore.eval = FALSE)
> cent$net.degree <- degree(nwk.s, ignore.eval = FALSE)
> t.test(net.degree ~ board, data = cent[cent$type != "President", 
+ ])

	Welch Two Sample t-test

data:  net.degree by board
t = 0.68327, df = 9.9063, p-value = 0.5101
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -3.728155  7.019891
sample estimates:
mean in group 0 mean in group 1 
       4.096050        2.450181 

> nwk1 %v% "board" <- cent$board
> nwk1 %v% "selfhits" <- nwk.size
> nwk1 %v% "type" <- nwk.type
> nwk1 %v% "region" <- as.numeric(cent$region)
> nwk1 %v% "presregion" <- cent$presregion
> nwk1 %v% "south" <- cent$south
> fit.ergm <- ergm(nwk1 ~ nodecov("board") + edges + nodematch("type", 
+ diff = TRUE, keep = c(1)))
Evaluating log-likelihood at the estimate. 
> fit.ergm2 <- ergm(nwk1 ~ nodecov("board") + edges + nodematch("region", 
+ diff = FALSE) + nodecov("presregion") + nodecov("selfhits") + 
+ nodematch("type", diff = TRUE, keep = c(1)))
Evaluating log-likelihood at the estimate. 
> m <- sum(nwk1 %e% "hits")/network.dyadcount(nwk1)
> nwk1.sum.init <- log(m)
> fit.ergmc <- ergm(nwk1 ~ sum + nodecov("board") + nodecov("selfhits") + 
+ nodematch("type", diff = TRUE, keep = c(1)) + nodematch("region", 
+ diff = FALSE) + nodecov("presregion"), response = "hits", 
+ reference = ~Poisson, control = control.ergm(init = c(nwk1.sum.init, 
+ 
+ 0, 0, 0, 0, 0), MCMLE.maxit = 200, seed = 1553659388))
Starting maximum likelihood estimation via MCMLE:
Iteration 1 of at most 200: 
The log-likelihood improved by 4.491 
Iteration 2 of at most 200: 
The log-likelihood improved by 4.205 
Iteration 3 of at most 200: 
The log-likelihood improved by 3.103 
Iteration 4 of at most 200: 
The log-likelihood improved by 3.863 
Iteration 5 of at most 200: 
The log-likelihood improved by 3.419 
Iteration 6 of at most 200: 
The log-likelihood improved by 0.7037 
Step length converged once. Increasing MCMC sample size.
Iteration 7 of at most 200: 
The log-likelihood improved by 0.0181 
Step length converged twice. Stopping.
Note: Null model likelihood calculation is not implemented for valued ERGMs at this time.
Evaluating log-likelihood at the estimate. Using 20 bridges: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 .

This model was fit using MCMC.  To examine model diagnostics and check for degeneracy, use the mcmc.diagnostics() function.
> fit.2012.logit.1 <- glm(board ~ net.ev + presregion + south + 
+ selfhits, data = cent, family = binomial(link = "logit"))
> fit.2012.logit.2 <- glm(board ~ net.degree + presregion + south + 
+ selfhits, data = cent, family = binomial(link = "logit"))
> nigeria2015 <- read.csv("nigeria2015.csv", header = FALSE)
> nigeria2015$degree <- NA
> for (i in seq(2, 2756, 2)) {
+ nigeria2015$degree[i - 1] <- as.numeric(as.character(nigeria2015$V2[i]))
+ }
> nigeria2015 <- nigeria2015[nigeria2015$V1 != unique(nigeria2015$V1)[2], 
+ ]
> names(nigeria2015) <- c("lastname1", "lastname2", "degree")
> nigeria2015.b <- nigeria2015[1:1326, c("lastname2", "lastname1", 
+ "degree")]
> names(nigeria2015.b) <- c("lastname1", "lastname2", "degree")
> nigeria2015.2 <- data.frame(rbind(nigeria2015, nigeria2015.b))
> nigeria2015.2 <- nigeria2015.2 %>% arrange(lastname2)
> nigeria2015.2 <- nigeria2015.2 %>% arrange(lastname1)
> nwk2015.s <- reshape(nigeria2015.2, v.names = "degree", timevar = "lastname2", 
+ direction = "wide", idvar = "lastname1")
> nwk2015.s <- as.matrix(nwk2015.s[1:nrow(nwk2015.s), 2:ncol(nwk2015.s)])
> cent2015 <- read.csv("nigerialist2015.csv", header = TRUE)
> cent2015 <- cent2015 %>% arrange(labels)
> rownames(nwk2015.s) <- cent2015$labels
> colnames(nwk2015.s) <- cent2015$labels
> shortnames <- function(x, y = cent2015$labels) {
+ str_split(y, " ")[[x]][length(str_split(y, " ")[[x]])]
+ }
> short <- sapply(X = 1:52, FUN = shortnames)
> nwk2015 <- network(nwk2015.s, directed = FALSE, matrix.type = "a", 
+ ignore.eval = FALSE, names.eval = "hits")
> nigeria2015.3 <- as.matrix(nwk2015, attrname = "hits", matrix.type = "edgelist")
> nwk2015.1 <- nwk2015
> delete.edges(nwk2015.1, seq_along(nwk2015.1$mel))
> nwk2015.1[nigeria2015.3[, 1:2], names.eval = "hits", add.edges = TRUE] <- nigeria2015.3[, 
+ 3]
> k <- max(log(as.matrix(nwk2015.1, attrname = "hits") + 1))
> nwk2015.col <- matrix(gray(1 - (log(as.matrix(nwk2015.1, attrname = "hits") + 
+ 1)/k)), nrow = network.size(nwk2015.1))
> nwk2015.width <- log(as.matrix(nwk2015.1, attrname = "hits") + 
+ 1)
> nwk2015.type <- as.numeric(cent2015$type) + 1
> nwk2015.board <- as.numeric(cent2015$board) + 1
> nwk2015.size <- cent2015$selfhits <- log(nigeria2015[1327:1378, 
+ 3] + 1)
> rightplotcoord <- read.csv(file = "rightplotcoord.csv")
> pdf(file = "Figure1-right.pdf", width = 7, height = 7)
> plot(nwk2015.1, edge.col = nwk2015.col, usecurve = TRUE, displayisolates = TRUE, 
+ coord = rightplotcoord, edge.curve = 0.01, edge.lwd = nwk2015.width, 
+ vertex.col = nwk2015.board + 2, vertex.lty = 0)
> segments(x0 = 16.3, y0 = -8.8, x1 = 20, y1 = -20, lwd = 2, lty = 2, 
+ col = 2)
> mtext(text = "Buhari", side = 1, col = 2, at = 20)
> dev.off()
null device 
          1 
> nwk2015.s <- log(nwk2015.s + 1)
> cent2015$net.bw <- betweenness(nwk2015.s, cmode = "undirected", 
+ ignore.eval = FALSE)
> cent2015$net.ev <- evcent(nwk2015.s, ignore.eval = FALSE)
> cent2015$net.degree <- degree(nwk2015.s, ignore.eval = FALSE)
> t.test(net.degree ~ board, data = cent2015[cent2015$type != "President", 
+ ])

	Welch Two Sample t-test

data:  net.degree by board
t = 3.7918, df = 48.896, p-value = 0.0004116
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 10.69962 34.83173
sample estimates:
mean in group 0 mean in group 1 
       27.16397         4.39829 

> nwk2015.1 %v% "board" <- cent2015$board
> nwk2015.1 %v% "selfhits" <- nwk2015.size
> nwk2015.1 %v% "type" <- nwk2015.type
> fit2015.ergm <- ergm(nwk2015.1 ~ nodecov("board") + edges + nodematch("type", 
+ diff = TRUE, keep = c(1)))
Evaluating log-likelihood at the estimate. 
> fit2015.ergm2 <- ergm(nwk2015.1 ~ nodecov("board") + edges + 
+ nodecov("selfhits") + nodematch("type", diff = TRUE, keep = c(1)))
Evaluating log-likelihood at the estimate. 
> m <- sum(nwk2015.1 %e% "hits")/network.dyadcount(nwk2015.1)
> nwk2015.1.sum.init <- log(m)
> fit2015.ergmc <- ergm(nwk2015.1 ~ sum + nodecov("board") + nodecov("selfhits") + 
+ nodematch("type", diff = TRUE, keep = c(1)), response = "hits", 
+ reference = ~Poisson, control = control.ergm(init = c(nwk2015.1.sum.init, 
+ 
+ 0, 0, 0), MCMLE.maxit = 200, seed = 1553659388))
Starting maximum likelihood estimation via MCMLE:
Iteration 1 of at most 200: 
The log-likelihood improved by 5.501 
Iteration 2 of at most 200: 
The log-likelihood improved by 4.821 
Iteration 3 of at most 200: 
The log-likelihood improved by 3.015 
Iteration 4 of at most 200: 
The log-likelihood improved by 3.087 
Iteration 5 of at most 200: 
The log-likelihood improved by 3.446 
Iteration 6 of at most 200: 
The log-likelihood improved by 2.83 
Iteration 7 of at most 200: 
The log-likelihood improved by 2.64 
Iteration 8 of at most 200: 
The log-likelihood improved by 3.983 
Iteration 9 of at most 200: 
The log-likelihood improved by 5.064 
Iteration 10 of at most 200: 
The log-likelihood improved by 3.265 
Iteration 11 of at most 200: 
The log-likelihood improved by 3.108 
Iteration 12 of at most 200: 
The log-likelihood improved by 3.505 
Iteration 13 of at most 200: 
The log-likelihood improved by 3.514 
Iteration 14 of at most 200: 
The log-likelihood improved by 3.166 
Iteration 15 of at most 200: 
The log-likelihood improved by 2.34 
Iteration 16 of at most 200: 
The log-likelihood improved by 3.23 
Iteration 17 of at most 200: 
The log-likelihood improved by 2.93 
Iteration 18 of at most 200: 
The log-likelihood improved by 3.992 
Iteration 19 of at most 200: 
The log-likelihood improved by 3.413 
Iteration 20 of at most 200: 
The log-likelihood improved by 3.657 
Iteration 21 of at most 200: 
The log-likelihood improved by 3.191 
Iteration 22 of at most 200: 
The log-likelihood improved by 2.471 
Iteration 23 of at most 200: 
The log-likelihood improved by 2.782 
Iteration 24 of at most 200: 
The log-likelihood improved by 3.309 
Iteration 25 of at most 200: 
The log-likelihood improved by 3.249 
Iteration 26 of at most 200: 
The log-likelihood improved by 3.286 
Iteration 27 of at most 200: 
The log-likelihood improved by 4.205 
Iteration 28 of at most 200: 
The log-likelihood improved by 3.734 
Iteration 29 of at most 200: 
The log-likelihood improved by 2.696 
Iteration 30 of at most 200: 
The log-likelihood improved by 2.5 
Iteration 31 of at most 200: 
The log-likelihood improved by 3.232 
Iteration 32 of at most 200: 
The log-likelihood improved by 4.611 
Iteration 33 of at most 200: 
The log-likelihood improved by 3.141 
Iteration 34 of at most 200: 
The log-likelihood improved by 3.363 
Iteration 35 of at most 200: 
The log-likelihood improved by 4.204 
Iteration 36 of at most 200: 
The log-likelihood improved by 3.348 
Iteration 37 of at most 200: 
The log-likelihood improved by 3.622 
Iteration 38 of at most 200: 
The log-likelihood improved by 4.612 
Iteration 39 of at most 200: 
The log-likelihood improved by 3.13 
Iteration 40 of at most 200: 
The log-likelihood improved by 3.024 
Iteration 41 of at most 200: 
The log-likelihood improved by 3.765 
Iteration 42 of at most 200: 
The log-likelihood improved by 3.216 
Iteration 43 of at most 200: 
The log-likelihood improved by 4.017 
Iteration 44 of at most 200: 
The log-likelihood improved by 2.882 
Iteration 45 of at most 200: 
The log-likelihood improved by 3.019 
Iteration 46 of at most 200: 
The log-likelihood improved by 3.076 
Iteration 47 of at most 200: 
The log-likelihood improved by 3.442 
Iteration 48 of at most 200: 
The log-likelihood improved by 2.895 
Iteration 49 of at most 200: 
The log-likelihood improved by 3.182 
Iteration 50 of at most 200: 
The log-likelihood improved by 3.106 
Iteration 51 of at most 200: 
The log-likelihood improved by 4.896 
Iteration 52 of at most 200: 
The log-likelihood improved by 4.293 
Iteration 53 of at most 200: 
The log-likelihood improved by 4.802 
Iteration 54 of at most 200: 
The log-likelihood improved by 3.339 
Iteration 55 of at most 200: 
The log-likelihood improved by 3.965 
Iteration 56 of at most 200: 
The log-likelihood improved by 3.448 
Iteration 57 of at most 200: 
The log-likelihood improved by 4.255 
Iteration 58 of at most 200: 
The log-likelihood improved by 3.533 
Iteration 59 of at most 200: 
The log-likelihood improved by 3.622 
Iteration 60 of at most 200: 
The log-likelihood improved by 3.567 
Iteration 61 of at most 200: 
The log-likelihood improved by 2.782 
Iteration 62 of at most 200: 
The log-likelihood improved by 2.786 
Iteration 63 of at most 200: 
The log-likelihood improved by 3.826 
Iteration 64 of at most 200: 
The log-likelihood improved by 3.078 
Iteration 65 of at most 200: 
The log-likelihood improved by 3.639 
Iteration 66 of at most 200: 
The log-likelihood improved by 3.127 
Iteration 67 of at most 200: 
The log-likelihood improved by 3.075 
Iteration 68 of at most 200: 
The log-likelihood improved by 2.562 
Iteration 69 of at most 200: 
The log-likelihood improved by 2.884 
Iteration 70 of at most 200: 
The log-likelihood improved by 3.336 
Iteration 71 of at most 200: 
The log-likelihood improved by 2.431 
Iteration 72 of at most 200: 
The log-likelihood improved by 2.571 
Iteration 73 of at most 200: 
The log-likelihood improved by 3.373 
Iteration 74 of at most 200: 
The log-likelihood improved by 2.853 
Iteration 75 of at most 200: 
The log-likelihood improved by 3.005 
Iteration 76 of at most 200: 
The log-likelihood improved by 3.936 
Iteration 77 of at most 200: 
The log-likelihood improved by 2.576 
Iteration 78 of at most 200: 
The log-likelihood improved by 4.46 
Iteration 79 of at most 200: 
The log-likelihood improved by 3.117 
Iteration 80 of at most 200: 
The log-likelihood improved by 3.87 
Iteration 81 of at most 200: 
The log-likelihood improved by 3.208 
Iteration 82 of at most 200: 
The log-likelihood improved by 3.964 
Iteration 83 of at most 200: 
The log-likelihood improved by 3.422 
Iteration 84 of at most 200: 
The log-likelihood improved by 4.027 
Iteration 85 of at most 200: 
The log-likelihood improved by 3.937 
Iteration 86 of at most 200: 
The log-likelihood improved by 3.326 
Iteration 87 of at most 200: 
The log-likelihood improved by 3.011 
Iteration 88 of at most 200: 
The log-likelihood improved by 6.361 
Iteration 89 of at most 200: 
The log-likelihood improved by 3.001 
Iteration 90 of at most 200: 
The log-likelihood improved by 3.398 
Iteration 91 of at most 200: 
The log-likelihood improved by 3.271 
Iteration 92 of at most 200: 
The log-likelihood improved by 2.823 
Iteration 93 of at most 200: 
The log-likelihood improved by 2.731 
Iteration 94 of at most 200: 
The log-likelihood improved by 3.502 
Iteration 95 of at most 200: 
The log-likelihood improved by 2.829 
Iteration 96 of at most 200: 
The log-likelihood improved by 3.684 
Iteration 97 of at most 200: 
The log-likelihood improved by 3.85 
Iteration 98 of at most 200: 
The log-likelihood improved by 2.811 
Iteration 99 of at most 200: 
The log-likelihood improved by 3.43 
Iteration 100 of at most 200: 
The log-likelihood improved by 3.589 
Iteration 101 of at most 200: 
The log-likelihood improved by 2.859 
Iteration 102 of at most 200: 
The log-likelihood improved by 3.329 
Iteration 103 of at most 200: 
The log-likelihood improved by 3.271 
Iteration 104 of at most 200: 
The log-likelihood improved by 4.186 
Iteration 105 of at most 200: 
The log-likelihood improved by 4.32 
Iteration 106 of at most 200: 
The log-likelihood improved by 3.838 
Iteration 107 of at most 200: 
The log-likelihood improved by 3.253 
Iteration 108 of at most 200: 
The log-likelihood improved by 3.704 
Iteration 109 of at most 200: 
The log-likelihood improved by 3.673 
Iteration 110 of at most 200: 
The log-likelihood improved by 3.662 
Iteration 111 of at most 200: 
The log-likelihood improved by 2.666 
Iteration 112 of at most 200: 
The log-likelihood improved by 2.842 
Iteration 113 of at most 200: 
The log-likelihood improved by 2.478 
Iteration 114 of at most 200: 
The log-likelihood improved by 2.288 
Iteration 115 of at most 200: 
The log-likelihood improved by 4.433 
Iteration 116 of at most 200: 
The log-likelihood improved by 3.281 
Iteration 117 of at most 200: 
The log-likelihood improved by 3.463 
Iteration 118 of at most 200: 
The log-likelihood improved by 5.271 
Iteration 119 of at most 200: 
The log-likelihood improved by 3.908 
Iteration 120 of at most 200: 
The log-likelihood improved by 3.343 
Iteration 121 of at most 200: 
The log-likelihood improved by 3.863 
Iteration 122 of at most 200: 
The log-likelihood improved by 3.286 
Iteration 123 of at most 200: 
The log-likelihood improved by 4.267 
Iteration 124 of at most 200: 
The log-likelihood improved by 5.999 
Iteration 125 of at most 200: 
The log-likelihood improved by 3.466 
Iteration 126 of at most 200: 
The log-likelihood improved by 3.536 
Iteration 127 of at most 200: 
The log-likelihood improved by 3.182 
Iteration 128 of at most 200: 
The log-likelihood improved by 3.592 
Iteration 129 of at most 200: 
The log-likelihood improved by 3.577 
Iteration 130 of at most 200: 
The log-likelihood improved by 3.873 
Iteration 131 of at most 200: 
The log-likelihood improved by 3.765 
Iteration 132 of at most 200: 
The log-likelihood improved by 3.497 
Iteration 133 of at most 200: 
The log-likelihood improved by 3.228 
Iteration 134 of at most 200: 
The log-likelihood improved by 3.568 
Iteration 135 of at most 200: 
The log-likelihood improved by 3.346 
Iteration 136 of at most 200: 
The log-likelihood improved by 3.132 
Iteration 137 of at most 200: 
The log-likelihood improved by 3.296 
Iteration 138 of at most 200: 
The log-likelihood improved by 4.846 
Iteration 139 of at most 200: 
The log-likelihood improved by 4.185 
Iteration 140 of at most 200: 
The log-likelihood improved by 3.462 
Iteration 141 of at most 200: 
The log-likelihood improved by 3.306 
Iteration 142 of at most 200: 
The log-likelihood improved by 2.925 
Iteration 143 of at most 200: 
The log-likelihood improved by 4.021 
Iteration 144 of at most 200: 
The log-likelihood improved by 3.839 
Iteration 145 of at most 200: 
The log-likelihood improved by 3.327 
Iteration 146 of at most 200: 
The log-likelihood improved by 3.239 
Iteration 147 of at most 200: 
The log-likelihood improved by 3.663 
Iteration 148 of at most 200: 
The log-likelihood improved by 3.417 
Iteration 149 of at most 200: 
The log-likelihood improved by 2.241 
Iteration 150 of at most 200: 
The log-likelihood improved by 2.441 
Iteration 151 of at most 200: 
The log-likelihood improved by 2.287 
Iteration 152 of at most 200: 
The log-likelihood improved by 4.87 
Iteration 153 of at most 200: 
The log-likelihood improved by 3.296 
Iteration 154 of at most 200: 
The log-likelihood improved by 3.445 
Iteration 155 of at most 200: 
The log-likelihood improved by 2.948 
Iteration 156 of at most 200: 
The log-likelihood improved by 2.569 
Iteration 157 of at most 200: 
The log-likelihood improved by 3.453 
Iteration 158 of at most 200: 
The log-likelihood improved by 3.179 
Iteration 159 of at most 200: 
The log-likelihood improved by 3.335 
Iteration 160 of at most 200: 
The log-likelihood improved by 2.664 
Iteration 161 of at most 200: 
The log-likelihood improved by 3.383 
Iteration 162 of at most 200: 
The log-likelihood improved by 3.477 
Iteration 163 of at most 200: 
The log-likelihood improved by 4.046 
Iteration 164 of at most 200: 
The log-likelihood improved by 4.121 
Iteration 165 of at most 200: 
The log-likelihood improved by 2.868 
Iteration 166 of at most 200: 
The log-likelihood improved by 3.23 
Iteration 167 of at most 200: 
The log-likelihood improved by 5.196 
Iteration 168 of at most 200: 
The log-likelihood improved by 2.77 
Iteration 169 of at most 200: 
The log-likelihood improved by 4.025 
Iteration 170 of at most 200: 
The log-likelihood improved by 3.892 
Iteration 171 of at most 200: 
The log-likelihood improved by 2.734 
Iteration 172 of at most 200: 
The log-likelihood improved by 3.25 
Iteration 173 of at most 200: 
The log-likelihood improved by 3.597 
Iteration 174 of at most 200: 
The log-likelihood improved by 3.698 
Iteration 175 of at most 200: 
The log-likelihood improved by 1.35 
Step length converged once. Increasing MCMC sample size.
Iteration 176 of at most 200: 
The log-likelihood improved by 0.04905 
Step length converged twice. Stopping.
Note: Null model likelihood calculation is not implemented for valued ERGMs at this time.
Evaluating log-likelihood at the estimate. Using 20 bridges: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 .

This model was fit using MCMC.  To examine model diagnostics and check for degeneracy, use the mcmc.diagnostics() function.
> clabs <- c("Board appointee", "Edges (density)", "Cabinet homophily", 
+ "Region homophily", "Pres co-ethnic", "Google self-hits", 
+ "Sum (density)", "Board appointee.c", "Google self-hits.c", 
+ "Cabinet homophily.c", "Region homophily.c", "Pres co-ethnic.c")
> cat("### --- Appendix Table 2 --- ###")
> texreg(list(fit.ergm, fit.ergm2, fit.ergmc, fit2015.ergm, fit2015.ergm2, 
+ fit2015.ergmc), custom.coef.names = clabs, dcolumn = TRUE)
### --- Appendix Table 2 --- ###Note: Null model likelihood calculation is not implemented for valued ERGMs at this time.
Note: Null model likelihood calculation is not implemented for valued ERGMs at this time.

\usepackage{dcolumn}

\begin{table}
\begin{center}
\begin{tabular}{l D{.}{.}{4.5} D{.}{.}{4.5} D{.}{.}{5.5} D{.}{.}{4.5} D{.}{.}{4.5} D{.}{.}{7.5} }
\hline
 & \multicolumn{1}{c}{Model 1} & \multicolumn{1}{c}{Model 2} & \multicolumn{1}{c}{Model 3} & \multicolumn{1}{c}{Model 4} & \multicolumn{1}{c}{Model 5} & \multicolumn{1}{c}{Model 6} \\
\hline
Board appointee     & -0.41       & 0.31        &             & 0.63^{**}   & -0.27       &              \\
                    & (0.33)      & (0.37)      &             & (0.22)      & (0.33)      &              \\
Edges (density)     & -3.00^{***} & -5.23^{***} &             & -2.66^{***} & -8.99^{***} &              \\
                    & (0.22)      & (0.43)      &             & (0.19)      & (0.61)      &              \\
Cabinet homophily   & 0.58^{*}    & 1.35^{***}  &             & 1.43^{***}  & 0.54        &              \\
                    & (0.28)      & (0.36)      &             & (0.21)      & (0.31)      &              \\
Region homophily    &             & -0.08       &             &             &             &              \\
                    &             & (0.38)      &             &             &             &              \\
Pres co-ethnic      &             & 0.65^{*}    &             &             &             &              \\
                    &             & (0.26)      &             &             &             &              \\
Google self-hits    &             & 0.39^{***}  &             &             & 0.82^{***}  &              \\
                    &             & (0.04)      &             &             & (0.06)      &              \\
Sum (density)       &             &             & -4.89^{***} &             &             & -12.49^{***} \\
                    &             &             & (0.33)      &             &             & (0.10)       \\
Board appointee.c   &             &             & -0.00       &             &             & -0.51^{***}  \\
                    &             &             & (0.29)      &             &             & (0.06)       \\
Google self-hits.c  &             &             & 0.38^{***}  &             &             & 1.15^{***}   \\
                    &             &             & (0.03)      &             &             & (0.01)       \\
Cabinet homophily.c &             &             & 0.84^{**}   &             &             & -0.13^{*}    \\
                    &             &             & (0.28)      &             &             & (0.05)       \\
Region homophily.c  &             &             & -0.17       &             &             &              \\
                    &             &             & (0.31)      &             &             &              \\
Pres co-ethnic.c    &             &             & 0.75^{***}  &             &             &              \\
                    &             &             & (0.22)      &             &             &              \\
\hline
AIC                 & 553.91      & 447.96      & -2155.86    & 1061.51     & 466.47      & -111254.90   \\
BIC                 & 569.48      & 479.10      & -2124.72    & 1077.08     & 487.23      & -111234.14   \\
Log Likelihood      & -273.95     & -217.98     & 1083.93     & -527.75     & -229.23     & 55631.45     \\
\hline
\multicolumn{7}{l}{\scriptsize{$^{***}p<0.001$, $^{**}p<0.01$, $^*p<0.05$}}
\end{tabular}
\caption{Statistical models}
\label{table:coefficients}
\end{center}
\end{table}
> fit.2015.logit.1 <- glm(board ~ net.ev + selfhits, data = cent2015, 
+ family = binomial(link = "logit"))
> fit.2015.logit.2 <- glm(board ~ net.degree + selfhits, data = cent2015, 
+ family = binomial(link = "logit"))
> cat("### --- Appendix Table 3 --- ###")
> texreg(list(fit.2012.logit.1, fit.2012.logit.2, fit.2015.logit.1, 
+ fit.2015.logit.2), reorder.coef = c(1, 2, 6, 3, 4, 5), custom.coef.names = c("Intercept", 
+ "EV centrality", "Pres co-ethnic", "South dummy (region)", 
+ "Google self-hits", "Degree centrality"), dcolumn = TRUE)
### --- Appendix Table 3 --- ###
\usepackage{dcolumn}

\begin{table}
\begin{center}
\begin{tabular}{l D{.}{.}{3.3} D{.}{.}{3.3} D{.}{.}{3.4} D{.}{.}{3.4} }
\hline
 & \multicolumn{1}{c}{Model 1} & \multicolumn{1}{c}{Model 2} & \multicolumn{1}{c}{Model 3} & \multicolumn{1}{c}{Model 4} \\
\hline
Intercept            & -0.96  & -0.89  & -1.81^{**} & -1.68^{**} \\
                     & (0.66) & (0.65) & (0.58)     & (0.56)     \\
EV centrality        & 2.96   &        & 1.52       &            \\
                     & (7.87) &        & (9.43)     &            \\
Degree centrality    &        & 0.11   &            & 0.02       \\
                     &        & (0.14) &            & (0.02)     \\
Pres co-ethnic       & 1.22   & 1.25   &            &            \\
                     & (1.11) & (1.10) &            &            \\
South dummy (region) & -0.31  & -0.43  &            &            \\
                     & (0.99) & (1.01) &            &            \\
Google self-hits     & -1.29  & -1.69  & -0.06      & -0.25      \\
                     & (1.05) & (1.12) & (0.36)     & (0.29)     \\
\hline
AIC                  & 51.80  & 51.21  & 47.06      & 46.09      \\
BIC                  & 61.55  & 60.97  & 52.91      & 51.95      \\
Log Likelihood       & -20.90 & -20.61 & -20.53     & -20.05     \\
Deviance             & 41.80  & 41.21  & 41.06      & 40.09      \\
Num. obs.            & 52     & 52     & 52         & 52         \\
\hline
\multicolumn{5}{l}{\scriptsize{$^{***}p<0.001$, $^{**}p<0.01$, $^*p<0.05$}}
\end{tabular}
\caption{Statistical models}
\label{table:coefficients}
\end{center}
\end{table}
